A Deep-Learning Model for Subject-Independent Human Emotion Recognition Using Electrodermal Activity Sensors

Fadi Al Machot · Ali Elmachot · Mouhannad Ali · Elyan Al Machot · Kyandoghere Kyamakya

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Emotion and Mood Recognition Context-Aware Activity Recognition Systems Face and Expression Recognition

Abstract

One of the main objectives of Active and Assisted Living (AAL) environments is to ensure that elderly and/or disabled people perform/live well in their immediate environments; this can be monitored by among others the recognition of emotions based on non-highly intrusive sensors such as Electrodermal Activity (EDA) sensors. However, designing a learning system or building a machine-learning model to recognize human emotions while training the system on a specific group of persons and testing the system on a totally a new group of persons is still a serious challenge in the field, as it is possible that the second testing group of persons may have different emotion patterns. Accordingly, the purpose of this paper is to contribute to the field of human emotion recognition by proposing a Convolutional Neural Network (CNN) architecture which ensures promising robustness-related results for both subject-dependent and subject-independent human emotion recognition. The CNN model has been trained using a grid search technique which is a model hyperparameter optimization technique to fine-tune the parameters of the proposed CNN architecture. The overall concept's performance is validated and stress-tested by using MAHNOB and DEAP datasets. The results demonstrate a promising robustness improvement regarding various evaluation metrics. We could increase the accuracy for subject-independent classification to 78% and 82% for MAHNOB and DEAP respectively and to 81% and 85% subject-dependent classification for MAHNOB and DEAP respectively (4 classes/labels). The work shows clearly that while using solely the non-intrusive EDA sensors a robust classification of human emotion is possible even without involving additional/other physiological signals.

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References

1994Measuring emotion: The self-assessment manikin and the semantic differential 1989A Concordance Correlation Coefficient to Evaluate Reproducibility 2011DEAP: A Database for Emotion Analysis ;Using Physiological Signals 2008A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions 2015Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks 2011A Multimodal Database for Affect Recognition and Implicit Tagging 2008Emotion recognition based on physiological changes in music listening 2013Deep learning for robust feature generation in audiovisual emotion recognition

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2023A new deep convolutional neural network incorporating attentional mechanisms for ECG emotion recognition 2020Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia 2022Current trends and opportunities in the methodology of electrodermal activity measurement 2021Deep BiLSTM neural network model for emotion detection using cross-dataset approach 2022

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